Fast ODE-based Sampling for Diffusion Models in Around 5 Steps

Zhenyu Zhou, Defang Chen, Can Wang, Chun Chen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 7777-7786

Sampling from diffusion models can be treated as solving the corresponding ordinary differential equations (ODEs) with the aim of obtaining an accurate solution with as few number of function evaluations (NFE) as possible. Recently various fast samplers utilizing higher-order ODE solvers have emerged and achieved better performance than the initial first-order one. However these numerical methods inherently result in certain approximation errors which significantly degrades sample quality with extremely small NFE (e.g. around 5). In contrast based on the geometric observation that each sampling trajectory almost lies in a two-dimensional subspace embedded in the ambient space we propose Approximate MEan-Direction Solver (AMED-Solver) that eliminates truncation errors by directly learning the mean direction for fast diffusion sampling. Besides our method can be easily used as a plugin to further improve existing ODE-based samplers. Extensive experiments on image synthesis with the resolution ranging from 32 to 512 demonstrate the effectiveness of our method. With only 5 NFE we achieve 6.61 FID on CIFAR-10 10.74 FID on ImageNet 64x64 and 13.20 FID on LSUN Bedroom. Our code is available at https://github.com/zju-pi/diff-sampler.